MongoDB, Inc. (MDB) Earnings Call Transcript & Summary

I mean, we're very excited. I think like anybody who's in this room, like they should just like go and maybe sign up for Emergent and just like build an app. So you get this kind of like not just see it in theory, but see it in practice. And like it's kind of mind-boggling, the kind of apps that people are building. We have people who are building mobile apps, which they've never written a single line of code. And like just prompting and like building mobile apps, shipping production-ready apps. So very excited to be in this world.

Well, that's so great. And thank you for sharing your story with us, and I appreciate you coming. Thank you.

Thanks.

So now I believe we're going to welcome our CRO, Ryan, up to the stage. Ryan?

Thank you, Ben. It's so great to be here this afternoon and also to see some familiar faces. For those of you who I have not met before, I've spent my career building and scaling enterprise go-to-market systems across consumption and cloud-native platforms alike. So the real question I'm sure you have for me today is why MongoDB and why now? There's really 3 key reasons behind my decision to join MongoDB. It's people, tech and timing. On the people front, we have one of the strongest operating benches in enterprise software today. Everyone on this leadership team has built and scaled global platform companies. And together, we have over 100 years of experience. On the tech side, not only does our JSON document-based model align well with the frontier labs, but specifically the way that we store and retrieve application context, it closely aligns with how agents consume information. Last, we are trusted for mission-critical workloads across financial services, health care, public sector and AI digital natives. And whether it's JPMC, Wells Fargo or Santander for payments or delivering global services for governments around the world, we are the trusted platform of choice. And the last is timing. AI tooling with partners like Cognition unlocks the modernization and migration of legacy Oracle, SQL and relational database workloads. We can do this now in days, not weeks and years. A year ago, these capabilities were just getting started. And now they're real. And I have to tell you, they are very real. Not only are we winning new AI workloads, but AI opens up a new TAM to over $10 billion to our teams to go after. For the reasons above, I truly believe this is the best time to not only be a MongoDB customer, as you heard on stage here, but equally be a leader at MongoDB. We are driving parity between Atlas and EA. We're delivering more core platform capabilities and products and doing so with greater velocity and greater reliability than ever before. And as many of you know, I was also the CRO at Confluent previous to the IBM acquisition. And while there are many similarities, specifically around consumption, and the equivalent of an EA or self-managed capability, workloads on this side, streaming projects on that side, this is a much larger opportunity. And without question, the market is coming to us especially with AI after a decade. Before we talk about the investments for the future, let's quickly take a look at the progress we've made to date. With Q2 earnings behind us, we have now delivered 6 quarters of consecutive Atlas growth at 29% year-over-year. Our self-serve continues to thrive, adding 25,00 to 3,000 customers a quarter, and we had a record of net new customers in Q2, totaling over 70,000 customers at MongoDB. I never had this at Confluent. And 17% year-over-year growth in our 100,000-plus ARR customers and our $1 million-plus ARR customers. I want to be super clear. This is a story of strength and momentum. We've seen tremendous traction over the previous 6 quarters, and now it's time to build on that momentum the team has created. To drive the next wave of durable revenue growth, we're making 3 strategic investments. We will focus our go-to-market efforts on the enterprise and executive buyer and our AI and digital natives. We're going to evolve our go-to-market system as we extend our capabilities and our platform with new products by adding product specialization, all those resources across all 3 global regions, and we'll be doubling down on our application modernization platform. And with our AI partners, you heard me mention, we will continue to identify and alleviate the modernization and migration pain that has plagued all of our customers for years. The path to $5 billion plus is through the enterprise executive suite. We are now evolving beyond a developer-led bottoms-up motion and I want to be super clear here as well. This is an and, not a replacement by adding equal focus on the enterprise suite that is driving enterprise-wide AI buying decisions. We will drive greater executive alignment by being laser-focused on our segmentation. We'll drive executive briefings and events, increase our forward invest for some of the new capabilities you heard about today as well as we will increase our incentives to our sellers to drive larger, more complex deals. We will go deeper in our accounts with our vertical depth, financial services, health care, public sector and there, again, a special focus on AI and digital natives. Regionally, we'll be investing in U.S. Fed. We're actively pursuing FedRAMP high, and we'll have some announcements shortly. At only 10% penetration today, we have nothing but headroom to grow. We'll be hiring a new U.S. Fed General Manager to accelerate the recent acquisition of Clarity. And we'll be making strategic geo investments in APJ and doubling down in Japan. We are establishing a new entity and hiring a new Japanese country leader, fourth largest economy, and it's been somewhat untapped by MongoDB, although you would be surprised at the current ARR without even having feet on the street. We expect this to be accelerated by a strong partner motion with my partner, Erica, as well. Now we also need to build our go-to-market muscle around product specialization as we extend our capabilities of the platform with the addition of new products. Specialization will allow us to go from incubation to scale with every new product without slowing our acceleration that we've had of Atlas and EA. We will maintain a strong core sales motion while driving the sale and adoption of new platform capabilities and products with the specialized team. We have developed the model now with specialists focusing on products like vector search, embeddings and re-rankings, that will allow us to repeat this and scale with every new launch. As you heard today, with the announcement of Atlas Engine, we will also increase our investment here as well. There's more to this than just adding resources. Specialization allows us to fine-tune our value messaging, our demos, our pricing and packaging and equally to ensure we have a fast feedback loop to our product and engineering teams as we compete. And here, there's no better prospect than an existing customer. And with over 70,000-plus customers, we have an enormous upsell, cross-sell opportunity for us to capitalize on. And lastly, modernization and migration. This represents a massive opportunity for this team. AI tooling lowers the cost and risk of legacy migration. We can do this now, as I mentioned, in weeks and not years, and it doesn't require armies of bodies as it has in the past. Equally, this modernization challenge has plagued our enterprise customers, as I mentioned, for years. We are actively migrating Oracle, SQL and other relational workloads to MongoDB via our application modernization platform and with partners like Cognition. Our path to driving durable and consistent growth to $5 billion and beyond is clear, engaging with the executive buyer, leaning into our vertical strength and geo-specific investments. We'll be strengthening our commercial muscle through platform specialization and capturing the modernization, migration wave all of our customers have been waiting for. We have the right leadership, now the right go-to-market system to drive the next wave of durable growth here at MongoDB. And before I turn it over to Erica, Dev is correct. We have the world's best go-to-market machine. And I have absolutely no question in our ability to drive and capture the next wave of growth for MongoDB. I'm now going to turn it over to Erica to go over our strategy with our partners.

Okay. Ryan, thank you. I'm thrilled to be here with all of you at Nasdaq. For those who I have not met, my name is Erica Volini, and I am the Chief Customer Officer here at MongoDB. In this role, I am responsible for ensuring an outstanding customer experience from technical support to customer success, to professional services, to our partner ecosystem, and that's going to be my focus for today. Now by way of background, I spent over 2 decades at Deloitte running 1 of their 5 global consulting businesses. In that role, I had a front-row seat to understanding what it takes for customers to truly drive digital and business transformation. After Deloitte, I pivoted from consulting into the world of tech joining ServiceNow, where I was responsible for scaling the partner ecosystem and, ultimately, their global go-to-market at a very strategic inflection point as they went from $5 billion to $10 billion. The through line of both of these experiences has been a deep focus on why and how customers make the decisions that they do. And that's something that I hold front and center as I focus on ensuring that we deliver an outstanding customer experience for our over 70,000 customers. Now part of bringing that remit to life is building a strong, trustworthy and innovative partner ecosystem because we know that ultimately, no one can win the AI era alone. We need MongoDB in every room where enterprise decisions are being made, no matter where a customer is on their agentic journey, whether they are modernizing their legacy systems and data, whether they're building agentic features into their applications, integrating agents, data and systems so that they can act in concert or scaling that capability across the business. Partners are guiding their customers at every step in the journey. And our goal is to be the platform they consistently rely upon. To do so means intimately understanding the expectations of our customers, expectations that have fundamentally shifted in the last few years. Let's start with value. Customers no longer measure a company by the output they deliver. Today, customers measure us by how we impact their business outcomes. That means being deeply ingrained in their business, in their technology landscape and being core to driving their AI transformations. Customers value business outcomes in large part because their AI initiatives aren't pilots anymore. In production, AI has to work. It has to be secure, durable, available and performant, delivering the business outcomes our customers are after. That's what our platform was built for. Our differentiators, though, they can't just be about technology because transformation today means more than a new system or a new platform. It means an entirely new way of working. To navigate this landscape, customers can't just rely on a single platform or a service provider. They need an ecosystem working together to help them succeed. That's why a strong partner motion here at MongoDB is nonnegotiable. That's not because we can't grow alone. We've proven we can, but because the fastest path to scale runs through partners enterprises already trust. We will see partners influence our revenue, our reach and our innovation through 4 primary levers. First, embedding our platform in every aspect of the AI ecosystem; second, becoming the default embedded data layer inside the agentic stacks of the leading system integrators; third, co-innovating with hyperscalers and Neo cloud; and fourth, becoming the intelligent data platform for industry ISV solutions. No matter what partner our customers choose, we want MongoDB to be the platform that powers their success. Our first lever is integrated across the full AI ecosystem. This is about making MongoDB easily and readily available within the companies that are redefining today's technology landscape. As you heard from Dev earlier, we look at the AI ecosystem across 4 major categories: coding agents, AI platforms, answer engines and AI frameworks. Across them all, we are investing in showing up wherever a builder needs us, whether that's an agent or a human. Like our connection with Devin, the AI coding agent from Cognition. With Devin, a team staring down a decade of accumulated legacy code doesn't spend the next 6 months manually mapping every table and dependency. Instead, Devin leverages MongoDB to work against live data instantly to transform the code in days or weeks, not months or years. Vercel is one of the largest distribution channels in the industry for how applications get built. Millions of developers use Vercel's v0 today. Well, MongoDB Atlas is now a native option in that flow, thanks to our managed MCP provisioned in a single click. So the agent building the app can query live data directly as it works. Mastra, fastest-growing TypeScript framework for AI agents now runs all of their data retrieval and memory on a single MongoDB Atlas cluster. That means a team can ship a production-ready agent without assembling and maintaining a patchwork of infrastructure. Each of these partnerships helps us embed MongoDB as the preferred data platform from day 1. LangChain is a perfect example of how this motion translates into both revenue and reach. Our customers can build generative AI and RAG applications on MongoDB through LangChain today. Now the partnership is still relatively young, but the revenue tied to LangChain is up more than 460% year-over-year. And builders are reaching for MongoDB inside LangChain projects 220,000 times each week. Now as important as each of these partnerships are individually, their true strength comes from the ecosystems that each of them has built, including our next lever, system integrators. All of the leading SIs, as you know, have built their own agentic stacks, often positioned as frameworks that incorporate the best-of-breed technologies required to drive a customer's transformation end to end. So when MongoDB is embedded as the data layer in these stacks, we get invited to transformation driven by the top of the house. Together, these top 6 systems integrators touch nearly every Fortune 2000 company on the planet. And Gartner estimates that enterprise AI engagements with SIs will increase by 15% by 2029. That's the access that these partnerships hand us. And that is why I am so excited that all 6 of these organizations have not only named MongoDB as a strategic partner for agentic delivery, but have already started to deploy Atlas Agent Engine internally, and the results are showing. One of these partners has already seen 984% higher query speed and a 63% latency drop when testing Agent Engine against sovereign AI offerings. Those are metrics that matter. But these partners don't just see the value of MongoDB for their internal use. They have also integrated Agent Engine into their agentic stacks that they're bringing to their customers. And they have identified use cases that span the entire enterprise from an agentic assistant for the oil and gas industry to an agentic business loan platform to agentic ticket management and technical support and agentic knowledge hubs that can search across policies, procedures and even medical documentation autonomously. Listen, I could go on. But what we know about these use cases is they span in all industries, and they're going to play a critical role in helping us drive that vertical depth and going deeper and wider across the enterprise, 2 of our go-to-market priorities that Ryan just spoke about. Now if you want to know why, why are these partners believing that MongoDB needs to be an essential part of their agentic stack, please look no further than the quotes right here. These are quotes not just from individuals at these partners, but the leaders of their global AI practices. They have become not just business partners, but close collaborators as we build and scale for this next era. And that leads me to our third lever, hyperscalers and neo clouds, the compute power that this next era relies upon. Nearly every workload an enterprise runs, every model they train, every transaction that they process sits on someone's cloud. Now Ben talked earlier about our Run Anywhere strategy, and that's what gives customers the flexibility to control where their workloads run and where their data lives, which means we need strong relationships across cloud providers of all types. With so many customers running Atlas on hyperscaler infrastructure, our teams are constantly collaborating on joint solutions and products. Roughly half of all MongoDB revenue is supported by a hyperscaler partner, and we're seeing marketplace revenue growing faster than overall revenue year-over-year. And that success shows up in the recognition we've earned. We've recently been named a Partner of the Year by AWS, GCP and Microsoft. At GCP specifically, we received Partner of the Year recognition for the past 7 consecutive years, and we're one of the top 3 Azure partners. Now I'm committed to continuing and expanding this streak. But as we all know, hyperscalers are not the only cloud business in town. The cloud provider market has expanded exponentially. We already work with many of the names you're seeing behind me, and we are actively pursuing relationships with others to ensure that wherever a customer's capacity lives, MongoDB meets them there. Specialty and neo clouds are reshaping the economics of AI infrastructure and moving up the stack. In MongoDB, we are uniquely positioned to support the scale, the speed and the data requirements of AI workloads. Our strategy is not only to launch new neo cloud relationships, but to evolve with existing partners as their platforms mature. As DigitalOcean, for example, expands from GPU infrastructure into an integrated AI native cloud spanning compute, inference, data and agents, MongoDB can act as the foundational data layer for these emerging AI applications. Our goal with hyperscalers and neo clouds is not just to integrate with them, but it's to co-build and co-innovate alongside each other, like with AWS, our largest cloud partner today. We're building with AWS and our own application modernization platform known as AMP to make legacy modernization dramatically simpler for enterprises stuck on decades-old systems. We are also, and I'm very excited about this in discussion with both AWS and Microsoft about leveraging the memory layer that Pablo talked about of Atlas Agent Engine directly into their AI platforms. Having this type of robust set of cloud partnerships, this really comes to life when we see it at our customers. Now Ben, I don't know where he is, talked earlier about Nationwide and their regulatory requirements. But what I want to talk about with this customer is what was happening behind the solution that got them where they needed to be. Nationwide needed to modernize their payments with 0 downtime risk. Now working at this scale, it doesn't happen with a single vendor. They needed the ecosystem, MongoDB as the data platform, Accenture as a systems integrator and multiple cloud providers running a single stretched MongoDB cluster across both simultaneously. Nationwide now moves GBP 1.5 billion in transaction volume every 24 hours, handling 3 million payments at peak, and we went from 0 code to fully in production in under 8 months. That's the nature of an ecosystem win. We make each other stronger and drive better customer outcomes than any of us could do alone. Now as much focus as there is on developers building applications, we also know that many enterprises out there are buying applications to meet their needs. And that's why the fourth and final lever is becoming the default data platform for industry ISV solutions because getting embedded in industry-specific solutions means that a customer that's won by the ISV is a customer won for MongoDB, and our installed base grows even further. Let's talk about what that looks like in practice. A security team leveraging BigID doesn't start their morning worrying where sensitive data might be hiding across a decade of acquisitions and half-documented systems. With MongoDB embedded, they run one search and see all of it, structured and unstructured before an auditor or an attacker finds it for them. A medical technician using software from one of the industry's best health care technology providers no longer stitches together one system to run a test and then another to manage the data behind it. It's one platform powered by MongoDB as the data foundation to keep it real time, accurate and, of course, safe. A customer messaging into an AI-powered support line has no idea what's happening in the background. They just get a better answer than they did last week. But behind the scenes, the model is learning from real conversations in real time. And because of our queryable encryption, we never actually see what was said. So we're able to keep customer privacy paramount. Someone with no engineering background opens Emergent Labs who you just heard from. They describe the app that they want to build in really plain English language, and they get a working product back. Grounded in, you guessed it, MongoDB's data platform. In 4 months, that's happened nearly 2 million times with more than 50,000 of these apps now in production. This is what winning through the ISVs look like. We are the batteries powering the Flashlight. Partners with deep industry expertise embed MongoDB in their solutions, so every customer bought into them is bought into us. So now we've talked about these 4 distinct levers and how each one works to expand our reach and expand our revenue. But to me, the story really isn't about each of these levers individually. It's about how they come together into one ecosystem. See, a customer on their agentic journey needs a full team behind them. They need a systems integrator. They need cloud providers. They need unique industry-specific solutions, and they need the best data platform in the world to underpin it all. Partners have the relationships, the trust, the industry expertise. But what powers all of it, that's ours. We aren't just building the products enterprises want. We're becoming the platform that partners build their own AI and modernization strategies on. That's the bet we're making on partners. And it's the exact same bet I made when I took the role of Chief Customer Officer. We don't scale by doing more ourselves. We scale by leveraging the best people, products and partners in the market to deliver what our customers need when they need it. Now hopefully, that all resonated because we're going to take a quick 10-minute break. And when you return, Mike is going to talk about what all this goodness looks like in the numbers. Thank you. [Break]

Thank you for your patience, putting up with the warm room a little bit. I had to take my jacket off. It got a little too hot. Before I get started on the safe harbor and go through some stuff, just 2 things. One, as you know, I like to pace. So if I'm in the way of the slide, just say get out of the way, and I will try to move. Jess did talk about, hey, these will get posted right when we're done, so you don't have to take a picture, you'll have access to it. The other thing, as Jess said it at the beginning, I just want to reiterate, folks, this is a big event for us, and it takes a lot of time, people and effort. It truly takes a village to use that term from Rita and Megan at the front that you saw there to the finance team that does all kinds of great work to the legal team that puts up with all of us. Oh, no, we can say that, right? To the marketing team who helps put this on. And then, of course, Jess and Will and the investor team. Thank you very much. It's truly a huge effort. Okay. So before we get into the financial update, once again, just on the safe harbor. As we said before, we will be making forward-looking statements during today's presentation. These statements reflect our views only as of today and are subject to a variety of risks and uncertainties. Please refer to our Form 10-Q for the quarter ended July 31, 2026, and other filings that we may make in the -- with the SEC for more details on the risks. So thank you for listening to that. Okay. Hey, I'm going to key off what Dev and everybody else said. And hopefully, you heard a lot of great things today. I know you did. But let me highlight a few of the things that you heard from the team. Dev's presentation talked about we compete in a very large growing market, and we have bless you, true discernible differences in the JSON native document model and our Run Anywhere strategy. We talk to you folks a lot about that. That's what really wins us business when we're in there, especially with the enterprises. Ben and Pablo did a great job sharing the exciting updates for our new innovations around Atlas, EA and AI. Ryan and Erica gave a great overview of the go-to-market priorities as well as our plans to significantly expand our partner ecosystem. So my job is to pull it together, what does it mean for financial metrics, and we could possibly talk about the long-term model as well. Okay. As I go through my presentation today, I'm going to break it into 5 sections. I try to do 3, but I had to do 5, so you'll have to bear with me. We're going to talk about the strong track record of results. Then we're going to shift to our growth that why we believe it is real and durable, I'm the CFO. So of course, I'm going to talk about operating margin and leverage, which is mainly driven by revenue growth. Then I'm going to talk about our AI focus and how we expect those product innovations to reflect in the financial model. And then after 3.5 hours, we'll talk to you about our financial targets. Okay. Hitting on our commitments. Hey, folks, we talked about it at the beginning. We, as a management team, take this very seriously. And credibility is built one quarter at a time and every quarter matters. And we take our commitments very, very seriously. So what we are going to show you is what we showed you last year and then how did we perform against those. So many of you are going to recognize this slide. So we've included the commitments from last year as well as how we did for '26 and then what the guidance is for fiscal '27. So last year, we talked about Atlas growth, 20% plus. In fiscal '26, rounded up, we grew by about 29% and our guide for this year is approximately 27%. While we did not specifically guide for EA and other, the implied growth in the guidance that we gave was low to mid-single digits. In '26, we grew at 7%. And in '27 guide, we've now increased that to 11%. We noted on the earnings call, this is the first time in 3 years that we are projecting double-digit growth for EA. That equated to total revenue growth commitment in the high teens. For fiscal '26, 22.8% and the guide for fiscal '27 is 21% to 23%. This will be the second straight year of acceleration of total revenue coming in at the high end. And then, of course, my next 2 favorite, operating margin commitment, and we'll talk about cash as well. So our commitment was, on average, 100 to 200 basis points a year. We went from 14.9% in '25 to 18.5% in '26, and we're guiding to approximately 21%. That was 360 basis points growth from '25 to '26 and 250 basis points for this year. We were super clear last year, the 100 to 200 basis points was not a ceiling. And we've talked a lot about the thing that I loved about Ryan's presentation is, man, there was a lot of investment in that discussion. It's going to result in great revenue at some point. We are continuing to invest in the business, and I'll walk you through a slide on that. It's got to -- we have a great business model. And when we drive revenue growth and still invest, it all falls to the bottom line. And then finally, free cash flow conversion. Jason, you've known me for a long time, Kirk, as you know, I love cash. It's my favorite. And cash flow conversion has been exceptionally good the last couple of years. We talked about 80% to 100%. Keep in mind that this is -- the business model is dependent on it in terms of how you collect from your customers. There's also this thing called working capital. And the team has done a great job managing both of those pieces. So fiscal '26, the cash conversion, which is non-GAAP operating income -- or I'm sorry, free cash flow divided by non-GAAP operating income, 108% year-to-date in '27, 108%, and we guided to the upper end of the range, okay? So as you can see -- sorry, I missed that one. We've hit every single one of our commitments. And again, we take this very seriously as a team to make sure that we do that. In addition to hitting our commitments, we also listened. I think in the first 2 months of my role here, we spent a lot of time on calls with you folks saying, what are the things that you would like to see better from MongoDB. And 2 things came up. Please give us a view of what you're guiding Atlas when you do your guidance. Don't make us do the math. So starting in Q2 of last year, we started to give that view. The other ask was, please share the ARR growth for EA and other because of the multiyear revenue, it jumps all over and please share that. So we started that in Q2 of last year, those 2 pieces. And then, of course, we introduced the long-term model, which hopefully has helped, especially you long-only investors, get a view of where we think the business is headed over the long term. Okay. Let's shift to growth, something we all love. Dev had this same slide that he showed earlier, and we showed it last year. It's a very large market. IDC estimates it's going to reach $200 billion by the year 2030, and it's growing at a compound annual growth rate per IDC of 12%. There are very few markets that are this big that grow this fast. And it was one of the things that I loved when I had my first conversation with Dev about joining MongoDB. It is a huge market. Both of these -- it includes both OLTP, which is obviously where we focus and OLAP. We look at really the OLTP market mainly, but we also look at OLAP as potential areas to invest in the future. We still have a very small share of the TAM today. Based on our guide, it's about 2.2%. There is a ton of room to run in this market. And as Dev, Ben and Pablo discussed, we believe we have the best data platform for the AI era, and we're very confident that we have a long runway ahead of us in this market. Okay. Let's shift to Mongo. This is the last several years of our revenue broken out between Atlas and EA. So over the last 5 years. Total revenue compounded growth rate, 28% on a market that's growing 12%, we are clearly gaining share, and that's a big part of our strategy. As you can see, the revenue growth has been strong and consistent. You can also -- one of the great results of this is a metric we talk a lot about, which is the 122% net dollar retention rate. And again, that's total company across both of these products. Our view is we have 2 independent healthy growth engines. Atlas, which has grown at a compound growth rate of 36% over this time frame and EA and other, and we added services in there to get the full picture, growing at a compound growth rate of 14%. Both of those over the last 5 years have grown double-digit growth. It is not a single growth engine story, and we feel it is durable because we have 2 strong growth drivers. This is my -- one of my favorite charts after cash. We talked about this last year, which is even though Atlas in fiscal '25 was growing very nicely, 27% year-over-year, we had flatlined as it relates to growing incremental dollars year-over-year. And for me, when somebody says the word acceleration, this is the slide I think about. We have now, for 6 straight quarters, added more dollars year-over-year each quarter. And that is a great testament to not only the sales team, to the customer success team, to the technical services team. And I'm going to show you a lot of stats on our customer growth that plays directly to this. And this growth has been across multiple vectors in our business. One is we do not believe it has been negatively impacted by the growth of VA, and we've been super clear about that. We've also seen growth across both self-serve, our wonderful product-led go-to-market motion as well as our sales sold. And as we've discussed, the majority of the growth in Atlas in the last several quarters has been driven by the larger enterprises, specifically in the U.S. and EMEA. And Erica touched on this, the marketplace offerings are a big part of our growth as well. And all 3 of the hyperscalers are growing very nicely, and it now represents approximately half of the total revenue of Mongo. This is a critical point because we get this question a lot. How do you compete against the hyperscalers? When someone deploys in the marketplace, everybody wins. We win and we get a customer, the hyperscaler gets consumption and the customers are able to burn down their commits. So that's one of the reasons why it continues to grow. Atlas is the biggest driver of growth. Guess what? EA also transacts in the marketplace as well. So it's a big driver of growth. As you look at that slide, those are the things continuing to push that up to the left. Okay. Let's shift to EA and Other, our self-managed product. This is over the last 10 quarters, the same time frame I just showed you on Atlas. As you can see, we were really averaging mid-single-digit growth until we saw an inflection in Q3 '26, and it's gone nothing but up at that point. Dev talked a lot about the industry drivers. Ben talked a lot about the product enhancements that we've made. Very importantly, all the new stuff we just built, it just rolled out a couple of months ago. So it has not really been a big driver of this. This has always been a strong product in regulated industries, but now we're starting to see other segments, especially technology as they're trying to deal with all the shifts in the industry that they want to run it on their own and self-manage it. The ARR growth has now been double digit for the last 3 consecutive quarters. And again, that was before we added search and vector search in Q2 of this fiscal year. We get this question a lot, so let's just get it out there. There's also a significant overlap with Atlas. If you look at EA customers with greater than $100,000 in ARR across Mongo, approximately 2/3 of them also use Atlas. So this issue of, hey, there's a cannibalization, and we see -- and we talked about on the last earnings call, we see that cohort actually growing faster than the total. So that it more Mongo is better across either EA or Atlas, okay? So now let's take a look at some customer metrics, and I'm going to move over so you can see these. Again, folks, you'll get a view of these. This is our total customer growth. Compound annual growth rate, 23%, driven mainly by Atlas growth, over 70,000 customers. Now let's break that down into the specific cohorts. If you look at customers that grow above $100,000 in ARR, it's grown largely in line with the total and a little bit more, but not much. Now let's go to the larger customers. If you look at customers, and Ryan talked about this as well, with greater than $1 million in ARR, you can see that, that cohort is actually growing much faster at 34%. So this isn't -- I'd like to do this because I'm going to give you a couple of new pieces of information, and this is one of them. This is the growth of the cohorts above $5 million in ARR, and it's growing at 57% compound annual growth rate. When I first started, this was the move that Dave and Cedric made to move upmarket to drive more growth, and we'll talk about it. So we get a lot of questions about, hey, can you walk us through the workloads and how they're scaling? And my answer to that is typically that doesn't matter, that does. Because they may deploy a workload, our job is to cross-sell, upsell and expand that customer, and this is what the move-up market has done. Now you say, great, Mike, hey, that's customer numbers. What does it mean for revenue? So this is another new one. I'm going to let you look at it. This is ARR starting in Q2 of fiscal '18 to the quarter we just finished. And it shows you the growth in ARR by customer size. And you can see the durable growth, especially in the larger customers, continue to drive the revenue growth. This is the move up market. This is the platform play. This is AI starting to take shape in enterprises that's driven such a big growth. And our job is to continue to get them to expand with Mongo even if their early workloads start to plateau. This is a customer play, not a workload-by-workload play, okay? I just need to get a drink, hold on a second. It's a great chart, isn't it? We had a bunch of different colors on there, and May said, no, Mike, they're going to throw up if you throw a purple on there. I put purple on there because it's my granddaughter's favorite color. And she said, "Hey, Grandpa, you're going to put purple on there, I'm like, no, sorry, I got to go with green. So we have a lot of conversations with you folks. The #1, actually #2 ask for metric, give us total ARR. You tell us what EA is. I've said repeatedly, Atlas growth largely mimics revenue growth mimics ARR growth and the ask was very simple. I cover a lot of companies, don't make me do the math. We did the math for you. This is subscription ARR ending Q2 '25, '26 and '27, okay? And as you can tell, it has accelerated each year, and they're both accelerating. Atlas growth consistent with revenue growth. We've given you this number before from a growth rate perspective, but there are the dollars. Spoiler alert, you're not going to see this every quarter, you're going to see it every year. So take a hard look at it. We'll talk about growth in revenue. But again, folks, Atlas revenue growth, largely consistent with ARR growth, and we give you the EA number as well, but those are the dollars. So hopefully, that helps. For us, this is a big part of the Run Anywhere story and the importance of self-managed because it is another growth driver, and it helps us drive total revenue growth, which is what I care about because it funds everything that we do. Okay. What does that mean for margin? This is another great slide. This is the revenue growth versus the operating margin. And during this time, we've increased and we talked about it, 360 basis points last year, at the upper end of the guide, 250 basis points growth. That revenue growth drives all of the operating growth. At the same time, we've invested in the business. So last year, OpEx was up, call it, mid-teens. This year in the guidance, it's almost 20% growth. And R&D is 30% in our guidance. The result of that 30% is what you just heard from Ben and Pablo in terms of the product innovation. Over time, we'll talk about this when we guide '28. There are areas of optimization, AI, offshoring and other things. But our goal is to continue to invest in growth because revenue drives operating margin expansion. So what are those areas we're going to invest in? Ryan went through a bunch of these in his, and you heard this as well from Ben and Pablo. Most of it has been on the core database and meeting our customers where they are as they make their AI journey. So no surprises here in terms of what we're investing in. We haven't talked a lot about Voyage, but we're also obviously investing in Voyage as well. This is rolling out 9.0. It's Atlas Infinite. It is our MCP -- managed MCP server and then, of course, new products like Atlas Agent Engine. I can see -- so used to saying EA, now I have to say AAE. So Atlas Agent Engine. And that's a big part of the investment. And then on the go-to-market side, Ryan went through a good bit of this, adding quota-carrying reps, especially in the enterprises to drive the data I just showed you, a stronger muscle around specialization for new products, expansion into specific geos. Asia Pacific and U.S. Fed are the 2 we've called out, investing in the partner ecosystem. Also and super importantly, folks, we continue to invest in developer awareness and supporting our AI initiatives. That -- go back to Reclaim the Bay is a big piece, and we're going to continue to invest in that as well. So a lot of this is in the -- all of this is in the 100 to 200 basis points growth that I showed you about 10 slides ago. So it's all baked into that. Okay. A small thing but super important for us is, hey, you want to be a business in my mind, you need to be GAAP profitable. And the company has invested for all the right reasons. We guided to be GAAP profitable, $0.77 at the high end of our guide. It's a part of it. A lot of this growth has been driven by the revenue growth, but also we've gotten a lot better about managing our stock-based comp, and I know a lot of you care about that as well. 30% 5 years ago, year-to-date, 20% as a percent of revenue. We're not committing to a range other than to tell you we know it's important. We will continue to manage dilution, and we expect this to go down. It may bump around a little bit if we do acquisitions or we want to invest everywhere else. We want the flexibility to drive growth. And everybody knows, especially for engineers, is a big part of their comp. So we want that flexibility, but we're mindful of the dilutive aspect of equity plans. Okay. Now my favorite chart, cash flow. Hey, when I started, and I looked at Mongo, and I struggled as to why the cash conversion was only about 50%. You generate all this profit, why isn't it going to cash? And there's 2 big drivers. One is the business model and product mix normalized. And as we went from EA to Atlas, the payment cycles there are just different. And you saw that in the deferred revenue, look at the cash flow, that was a negative impact to operating cash. That's largely modulated as that has normalized. The other part is what we can all, and I give all 5,700 employees at Mongo a lot of credit. We can manage our working capital a lot better. I never get a vendor anymore that says I'm going to pay you in net [ 30% ]. No, we're going to pay you a lot longer. And it's just like everybody else. So it's been the normalization of the business as well as managing working capital. Folks, we generated almost $500 million of cash last year. And our goal and our desire is to continue to generate meaningful cash flow. It gives us a lot of flexibility around the share buyback as well as using our cash to grow. So huge slide for us. Okay. Before I get to the financial targets, I do want to hit AI and new product innovations. So we've had a lot of debate about this, and we get a lot of questions from you folks. Tell us how big AI is in your business. So this is a point in time we will define for you exactly how we define AI natives and frontier labs. Here's my -- I'm almost assured when we stand up here or whatever next year, that definition will change the market is moving too fast. So it is a point in time. Please don't expect we're going to update it every quarter. But we want to give you a view of how big it is now with the full provide, though, which is we make no promises to keep the same definition. Don't yell at me if we move it around, the market is moving too fast. How do we define AI native and frontier labs? Those companies that model R&D, whose core product is their own foundation, i.e., frontier labs. That's one. AI infrastructure companies whose core product is the physical or software infrastructure that AI models run on. And number three, AI-native software companies whose software application would not exist without generative or deep learning AI. Those are the 3. We do our best to audit it. Everybody says they're an AI company, fantastic. We use outside services as well. This is our best guess as we sit here today. So all these metrics key off of that definition. Okay. Let's jump into it. On the left, you see, and we've talked about this, what is the percent of customers that are using multiproduct? And all these products are focused almost always on an AI use case, search or Vector Search, and we've talked about that 34% to 48%, basically from 1/3 to 1/2. These are for customers with more than $100,000 of ARR. So these are the biggest customers. And then you look at Vector Search, which is a big piece of it, and Pablo talked about this as well. Customers that use Vector Search in the last 2 years have grown by 4x. It's a huge growth in the customer base. Vector Search, as you know, powers AI agentic use cases such as REG, semantic search and agent memory is a big piece of it. So lots of good signs in the customer base. Last year at Investor Day, we talked about what percent of Atlas ARR customers have at least one AI use case. And we define this. Are they using Vector? Have they raised their hand and said, we want to be part of your programs? Last year, it was 30%. This year, it's 40%. And this is when we talk to you folks about, hey, we're starting to see enterprises deploy AI at least internally, and these are some of the use cases that we see, and it's across a mix of enterprise and AI native customers. So we get this question a lot. What are the top use cases you see in your customer base deploying AI? So I'll give you the 3. Giving their systems accurate real-time context from their company's own data, making sure the data can support their AI initiatives. Number two is running autonomous AI agents. And number three is AI-powered customer support. Those are the 3 big use cases that we see today. That's going to change over time, but that's where we are today, specifically focused around enterprise customers. All right. I'm going to give you a second to stare at this chart. On the left -- your left is the number of customer count in the AI native cohort that I just defined for you. On the right is the revenue for Atlas and the percent growth. As we sit here today, AI natives make up less than 5% of Atlas revenue. I'm just going to cut the question off. When you think of that, think of 3% to 5% as we sit here today, okay? But look at that incredible growth, 65 (sic) [ 67% ], 129%, 165%. We talk a lot about this, which is it's still small, but it's growing very, very quickly. And this is why we're comfortable with this continuing to drive growth in the future, and it's off of a small base, but it's super powerful. We showed you the names, you know who some of the customers are. And a lot of our product innovations are designed to drive that number up. So total customer count doubling in the last 2 years, revenue for Atlas growing at 165% year-over-year, and it's about 3% to 5%. So I never have to answer. We don't -- it's just a small number. There it is. There it is. Okay. And we're spending a lot of time and effort extending our lead in the AI universe. Okay. We introduced 3 new big things today, new features on EA, Atlas Infinite and Atlas Agent Engine. We've talked a lot about EA features and how we monetize that is incremental EA monetization through new search and Vector Search licenses. If you want that functionality, you need to get -- you need to basically sign up for it. We'll give it to you, but you're going to pay for it. Early revenue contributions should start in fiscal '27, but build much more in fiscal '28. On Atlas Infinite, it is so well suited to agentic workloads. And as Ben shared, hey, these are spiky, bursty workloads and Infinite is a great solution for that. It delivers extreme elasticity, which again is perfect for those workloads. It just went into public preview. We expect at some point in '28 or in early '28 that it goes GA, but it's going to build throughout '28, okay? There's a lot of development that still needs to happen. So don't look for meaningful revenue in '28, but certainly in '29. With all of these new products, we hope we go faster, but that's the base case today. Atlas Agent Engine, and Pablo talked a lot about this, we feel really good about the product positioning. And to reiterate some of those points, it delivers the capabilities that we think is needed in the market. It provides agent memory so agents can learn. It has the governance and guardrails that keep agents safe and compliant and it's open and flexible. And it's for builders, which is who we market to. So this is a new component. The monetization of this is it's going to drive more Atlas consumption. As Pablo talked about it, everybody can use it in the public preview now. The expectation is that GA is either later this fiscal year or early next fiscal year, and we should start to see some momentum in fiscal '28, okay? So those are the 3 new products. That's how we view those today. We haven't guided '28. We'll certainly let you know how these play in. I think the number you'll see move the most is EA and then the other 2 following as we go through '28. You've been super, super patient. Let's jump into it. These are the long-term targets that we showed last time, and let me just run through them. Importantly, folks, we're in a 3- to 5-year time frame. That was our first long-term model. So this is from last year, this is not new. We were making sure to give you a view of where we thought the business was moving, and we picked 3 to 5 years. And again, total revenue growth, high teens, Atlas revenue growth, 20% plus, EA and other implied mid-single digit and then operating margin of 100 to 200 basis points, not a ceiling and then 80% to 100% free cash flow. We did not talk about GAAP profitability, but we will today. So in the new model, we have shortened the time to 3 years. Folks, 5 years is a long, long time. This gives us the capability to be a little bit more pragmatic about what we think the next 3 years look like, and we don't have to have the hedging of 5 years is way, way too long. So that's important because it narrows the time frame. So let's start at the top. Our new long-term target for total revenue growth is 20% plus. We feel very confident that we have 2 growth drivers. And we'll talk about each one of them, Atlas and EA, and we are very comfortable saying that we expect to grow total revenue north of 20% for Atlas. We have increased our long-term target to mid-20%, okay? This is an important metric. So let's talk about this a little bit. Our old target was 20% plus. Our new target is mid-20s. Keep in mind, we are using the same guidance framework that we've talked about the last 3 quarters. If we've been clear about one thing, it's the guidance framework that we have for Atlas and for EA, and we've not changed that. We continue to feel very good about Atlas. It is a consumption product, and we'll always be prudent in our projections, and we've told you that. And again, it's a little bit shorter time frame, but it is a meaningful jump in terms of how we think Atlas should grow. And as we've said, folks, we're going to be prudent in our forecasting and guidance. That being said, we get a lot of questions about how can Atlas grow faster? And what we've listed here are what those targeted sources of upside are. And we've talked a lot about this. Better enterprise AI adoption is probably #1. AI native contribution, it's 3% to 5% now. Boy, we would love that to be much, much higher. The new products, if they get going faster and we start to see better traction, that will help. App modernization is the goal mine we're all chasing. And now with our new app product, we feel much better about that. That is an upside and then all the great partnership motions that Erica talked about as well. So in our base case, these are all here, but they're all -- if they do better, then hopefully, we can grow Atlas faster, okay? So that's Atlas. Let's shift back and talk about EA and other. We all know the nuances of the multiyear revenue impact to EA. And I've said from the time I started, I'm not going to lean over and bet on a multiyear deal because the minute you do that, you don't get it and I look like a fool in front of all of you. We are basing our long-term targets on ARR because it mitigates the multiyear impact. But what we're committing to is 10% plus growth in EA. That is not a ceiling either. We expect double-digit growth if all the new products pick up and the world continues to move, hopefully, that's even higher. But at a minimum, we expect to grow ARR over the 3-year time frame at 10% plus. Operating margin is the same at 100 to 200 basis points. And it is not a ceiling folks. What I want to make sure you realize is we walked you through the business model. We want to invest to drive growth in product innovation and in go-to-market. And I don't think we have a margin problem. We want to drive better growth. We expect the business model to continue to drive up margins, but we also want to be able to invest in the business. So we're going to leave it at 100 to 200 basis points. The last year, 2 years ago, it was 350 basis points, 260 basis points, it's not a ceiling. And the model has great leverage. And the same thing for cash flow, we're going to leave it at the same 80% to 100% because, again, so much of this is driven by the pricing and the collections from customers and folks, we don't want to go down that path. We feel good about where we are in our pricing, and we expect the business model and the product mix to be relatively normalized. And then very importantly, we fully expect to be GAAP profitable in '27 and in the future. So you add all that up, this year on the Rule of 40, we've guided to the Rule of 44. Here's what we say. If we do what we say we're going to do and maybe a little bit better, gosh darn, we get pretty close to the Rule of 50 exiting the 3-year. That is our goal. I'm not going to commit to it because, again, I want the flexibility for those things to move around a little bit. So we feel really good about this. We get a lot of questions, how do you feel about the business? We feel really good about the business. And that's a commitment that we're making over the next 3 years. Okay. Before we go to Q&A, let me talk about capital allocation, just to close this off. We continue to expect to drive material free cash flow. That then we will use to help manage dilution from the equity plans. It's not going to completely offset it, but we do expect to manage dilution through the cash flow. And then we have over $2 billion of cash on the balance sheet. It's not burning a hole in our pocket. We will look to do targeted acquisitions that are focused around product road map or open market adjacencies. And I missed one thing, which is I'm very happy to announce that the Board of Directors has approved another $1 billion in our repurchase plan. So thank you for that. That's all part of the stock buyback, and we are announcing that today. So we will target disciplined acquisitions that are either tech and talent or bring new adjacencies to the business like we did with Voyage a couple of years ago. Okay. Key takeaways from the day from my perspective. It's a huge market opportunity. We have strong momentum in a large and growing market, and we still have a very small share. Our core products, we are confident in the durability of growth of both of them. AI is early. New innovations are early, but momentum is building and the new innovations strengthen our right to win. The business model continues to generate operating margin expansion and fully supports the new long-term targets. And potentially most important after all the questions in the last 2 days, folks, we think we have -- we know we have the team to execute. We have the right team in the right place to hit our commitments. We take very seriously our commitments to 70,000 employees, 80-plus million shares outstanding and our awesome 5,700 employees, and we are proud to stand in front of you today and tell you the plan for all of those folks. So we're excited about Mongo. And at that, it's time for Q&A, and I'm going to ask Jess to moderate. If I could ask the management team to come up, that would be great. Thank you.

We've got about 45 minutes for Q&A here. I'll just give a run of show here while the team comes up, direct your questions to either Dev or Mike, and then they'll chunk them up with respect to the answers. Jason, do you want to kick us off here? State your name and firm name.

All right. Jason Ader with William Blair. I wanted to ask about the Atlas Agent Engine. And in particular, like what does this compete with today? Is it replacing something? Just maybe talk about what people are doing today in terms of that agent narrative.

Yes. So what we see when we talk to a lot of customers, especially in the early days is they're using a host of different solutions. I mentioned some of them on the slide, you'll see hyperscalers SaaS solutions, some of the LLM providers and SaaS solutions and then there are also some that are verticalized around analytics or SaaS. What we've heard from customers is in a lot of these different solutions, the building blocks, like if you're using a hyperscaler, as example, who provides this, they provide you with all the building blocks, but you have to stitch this all together and put a lot of onus on the builders around some of the requirements for security, for being open, which a lot of the hyperscalers and LLM providers don't provide that flexibility. And so what we've seen is it really comes down to 3 things from a competitive landscape where we think we have a differentiated solution. As you heard from Vikas, memory and getting memory to context, which we do natively and configurably for our customers is number one. The second one is we don't lock you into a model or we don't lock you into a cloud. So we really do provide that open framework. And then because we had a chance to listen to customers who are playing around with different solutions early on as we were going through design, we took security as the first principle and actually built it into the system. So you don't have to actually build security on top or bolt it on top. It's actually part of the system. And that's really important for customers, especially a customer like Citizens Bank that's in a regulated industry.

Tyler Radke from Citi. Great presentation and I appreciate, Dev, you being here in person. Maybe I'll direct this at you, and it probably applies to the broader group. But one of the things that we've heard you guys talk about as it relates to AI and why it's maybe a little bit earlier in the journey for Mongo versus some of your peers is many of the AI applications built were more employee-facing kind of back office facing. I wanted to ask you now that we've seen a lot of momentum, Meta Muse, some of these consumer-facing and customer-facing agents really rise into production. How do you see Mongo's role within those use cases? I know Meta, for instance, uses PostgreS, but is that an opportunity to potentially win that in some type of partnership? Just how do you think about that opportunity broadly?

Thanks, Tyler. And I'll start, and I'll ask the team to jump in if I miss anything. So I think I said this right at the beginning. I think, one, when you use an agent to reason about a question or a decision, they need to have access to fresh data. So the freshest data in your system is live operational data, information about your business, what your transactions you're executing, what orders you're taking, what analytics -- not analytics, but sensor information you're getting about your business. And so one is like you need to have fresh data. MongoDB is an OLTP data store. Two, just because you have fresh data, it doesn't mean you can find the right information because you can have a lot of data. So being able to precisely retrieve that information based on what's being asked is super important. So like the fact that we have a very sophisticated retrieval mechanism with Voyage and using embeddings and rerankings to basically -- think of it as like if you told someone, I want you to find information about something and go read the library. If they spend -- read every book in the library, that would be a very expensive process. But if you tell them, go into this section, it's about chemistry, it's about organic chemistry, and I want you to find this particular topic, all of a sudden, very quickly, you find the relevant information. So finding information quickly and precisely is important. We are world-class at that today. Three is scale. Like these -- as we talked about and as Ben talked about, the unpredictability of these workloads is very high. And so -- because now you don't know when the agents work, like obviously, for users after everyone goes off -- goes home, you see traffic come down. You have a kind of a good pattern of traffic. But agents could be working all night in an unpredictable manner. So you need a platform that can elastically scale. With 9.0 and Infinite, we think we are the most performance and scalable OLTP in the marketplace. And then last but not least, you also need to know state because you need to understand what happened and when and keep understanding the history and what you're supposed to do next. And if you don't understand state, things can go awry. And obviously, with the memory capabilities we're rolling out. So we have all the ingredients to really position ourselves well to go after this market. Again, all these things have come out recently. So -- but we think there's going to be a ton of other exciting potentially new AI workloads that we can really go after.

Maybe I'll just say 2 things. One, with some of the frontier labs. One, we have seen, as they get into production scale, needing both the operational real-time data and also being able to drive it at scale, moving from solutions like PostgreS onto us, both across pre-training and post-training. So I think there's a journey for them. And the second thing I'll say, which we hear from customers is a lot of customers look at these pieces as composable blocks. And so they may use a cloud agent from one of the frontier labs or from a hyperscaler, but we are providing all of Atlas Agent as composable components. So if you want to own your data, if it's core to your knowledge, you can actually put it into one of those systems and we'll just work natively within that cloud. So it gives customers some flexibility.

Rishi Jaluria, RBC. This is super helpful with all the information. Mike, in your remarks, when you're pointing at the TAM slide, you kind of hinted about maybe wanting to invest potentially more into OLAP as we think about the TAM. I know in the past, you had Atlas Data Lake, I think that's now called Atlas Data Federation or been folded into there. Maybe can you talk about what could that look like? Is there -- is that kind of a demand that's starting to come from customers? And the flip side of that question that's always going to get asked is we've seen some of the OLAP vendors try to get into OLTP via M&A. Not too much has happened out of that, but maybe just how should we be thinking about both of those forces?

Yes. Thanks, Rishi. I'll start, and I'll ask my friend, Ben, to jump in as well. So we are focused on OLTP. That's in building out that platform. That's the majority of the R&D. That's what we're focused on. When we look at M&A for tech and talent, it's largely road map accelerators in there. That being said, there's a big market there. There is now starting to be a little bit of a gray area in that. So we'll look at it. But -- we also don't want to get diffused outside of our core focus. We think the market is so big in our core market and the growth is there. So we'll look at it. We'll be disciplined, but that would be a big step for us to step outside that. And again, we've got great sellers. We want to keep them focused, and that's the big piece. But you want to talk about our friends coming into our group?

Yes. And for everyone who's listened to me talk for the last 12 months, you know I describe this as cute that OLAP players think they could be credible in the OLTP space. And it took us a lot of years to do that. And I think, if anything, then we've been saying from the very beginning of the AI journey that OLTP was the higher ground for AI. then getting into OLTP is just asserting what we knew from the very beginning.

Ryan MacWilliams, Wells Fargo. Good to see another Ryan Mac. So really great to see the growth since from Mike in the AI customer cohort in Atlas, like really strong growth in that segment. So what are you seeing in both your AI Atlas customers and your core Atlas customer segment? Those 2 segments that help give you confidence in the new multiyear Atlas market.

Yes. So as we've said it for the last several quarters, we feel really good about the Atlas business. It's growing at 29% consistently. And a lot of what we've seen is not only the incremental adds. It really goes back, Ryan, to the customer piece that we talked about in terms of the growth within our existing customer base, the expansion there. We get a lot of questions about workloads. And yes, workloads will plateau. But the team, Erica's team and Ryan's team have done such a wonderful job of cross-selling and upselling. We're adding to the platform less still of half of our greater than $100,000 ARR customers using multiproducts. And we've talked about this. There's a lot of product adoption. There's not a ton of revenue generated yet because it's going to be lower than that. That is an upside as well. All the new products we talked about, even though we have marketplace in Japan, we should be a lot bigger. U.S. Fed has a huge growth opportunity. So you look across that whole area and Voyage, and that's why we have confidence in the multiyear outlook. And the other piece is, and I just want to address it, look, we didn't have a CRO for 6 months, and now we do. And CJ, bless his heart, really had to jump into that void. Now we've got him. And the great part about that is Dev has the same relationships, awesome, but now we have a guy to execute on it. And that's the other thing. We feel much better about where go-to-market is sitting here today than we did last year because of the coming transition.

Siti Panigrahi from Mizuho. Congratulations on that Atlas Infinite launch. It was great to see the architecture, how you decouple memory and storage. So the question is, how are you going to position? There are certain applications can be both agentic and user workloads. So how are you going to differentiate that positioning? And as customers do some kind of spend optimization, do you expect some of the Atlas Core customer switching to the Infinite?

Yes. I think from -- on the positioning side, first of all, remember what we announced today is preview, right? Just on AWS. We have a ways to go. We're going to add the other clouds and everything. So it's still early. As I said, Infinite is definitely designed to handle the bursty and unpredictable nature of agents and scale. That doesn't mean that it's only for that. We see different levels of scale all the time depending on customers' workloads. Sometimes it's a steadier peak. It all kind of depends. What my expectation is that, one, because it's preview, and two, since Atlas Dedicated clusters has been out now in the market for 10-plus years, I don't see a mass movement of existing customers moving to Atlas Infinite. They're happy where they are. It's a predictable pricing model. There's no complaint, right? As they start working with data in agentic workloads, my guess is those workloads will start out earlier on Infinite. And because we're giving the customers' flexibility of going back and forth, however they want to. Remember, guys, portability, #1 thing I've been saying, go on-prem, go into self-managed, go into Atlas. There's going to be movement and that's what the flexibility we're providing. So I don't think this is going to be like, "oh, sh**, I can optimize. I'm just going to go fast forward there." I think it's going to be slow, and I think it's going to be allow customers to prototype more and allow them to move data around.

Ivan Feinseth, Tigress Financial Partners. What do you see as driving your biggest competitive wins? Is it new application development? Is it data migration, data consolidation? And what do you think are your advantages that are driving that? And then also, what do you think is on the horizon that's the next big thing that also you're best positioned to benefit from?

Look, we've done a really good job of getting into one line of business with a customer. And now we have a seat at the table at a more executive level, and they're looking at it across now multiple lines of business. I would tell you that's a huge strength for us to go upsell and cross-sell. As you heard from Mike, too, and the team, adding new platform capabilities, they're looking at our road map and saying, wow, okay, this is where I want to be. And I also mentioned I've been doing this long enough to where there are so many customers that have so much technical debt that they've had backlogs that they want to modernize and migrate and they're taking those projects on because now the AI tooling and capability is there for them to do so and being able to migrate in literally days and weeks instead of years. So it's all of those things together, I will tell you.

Patrick Colville here from Scotiabank. Lots of juicy, fantastic metrics. To me, the most interesting was the 3% to 5% from AI natives. I guess to me that metric speaks to the success, but also the opportunity because I compare it to another infrastructure company headquartered in the city, and they're at 3x that level, right? So I guess maybe for you, Mike, and also for the broader team, what would it take for that metric to get to the double digits, the mid-teens point contribution from AI natives?

Yes. Thanks for the question, and I'll let you ask them all that piece of it. For us, look, it is -- we've said it, Patrick, the whole time, which is it's still a relatively small piece. It is workload by workload in there. And if you look at AI natives and frontier labs, they're going to be different. We love the AI natives. They're all still pretty small. You heard of one today, Emergent, which is doing a nice job, and they're growing. So for us to get that in double digits, it's really 2 things. It is more workloads in the larger labs, clearly. And then that AI native cohort starting to scale and starting to grow as well because those are the workloads that we should scale with them as they grow their business. And continued customer acquisition. We've talked about this. This is where the new customer metric comes in. You saw that double the number of customers that are AI native. That's going to expand as well. So it is still small, growing very quickly. For us, that's a relatively good thing because we also don't have dependency on any one, and we do expect that to continue to drive growth.

Kirk Materne with Evercore. I think this is probably for Ryan, but you all are obviously pursuing sort of a dual-pronged strategy, obviously, getting bigger with your biggest customers at the same time going after builders. How do you think about just monetization and pricing along those 2 different tracks? Obviously, your bigger customers have to think about security, statefulness. Smaller customers just might want to go. And I was just kind of curious how you think about making sure that you're thinking about monetization the right way across those 2 vectors and anybody else that wants to add to that?

That's a great question. Look, it comes up all the time, and it's something that Erica and I continually look at, as well as with the engineering team. First, it starts by listening, right? Listening to our customers, whether it be one that comes through self-serve and is somewhat small and from our largest customers. The flexibility that we have, though, with both Atlas and EA today is pretty helpful as we see more customers focusing on a hybrid approach, I will tell you. And then it's really just listening. So we're going through that, and we will continue to evaluate that, and we'll make sure that we adjust accordingly, right, with keeping an eye on margins.

Yes. I think, Ben, maybe you can just talk a little bit about how our pricing approach is much simpler than some of the other infrastructure providers.

Yes, for sure. So a couple of things is that a lot of the other infrastructure providers doesn't matter which one, they bill customers, especially in cloud as by read units and write units. We don't do that, right? Customer comes in, and I'll just talk about Atlas Dedicated because it's the GA product. You pick your capacity, you pick your storage. If you don't exceed that capacity, your bill is the same every month, and it's predictable. Customers love predictability. Enterprise customers love predictability. But what this also means is as enterprises start to prototype different types of workloads or they add a little bit more data, unless they exceed those bounds of that -- the box, so to speak, we're not going to charge them more. So on the weekends, they want to try an agentic workload and throws 100,000 queries at it. They didn't exceed their allocate. Great. And so I think it's really important to understand that like one of the reasons why enterprises love us so much besides the security and the governance and compliance and all these things, is that we provide them a very predictable surefire way that they can actually understand their spend, understand their usage, and they're not worried about the runaway bills that we've heard from the past.

Raimo Lenschow from Barclays. The big debate in the market is people start working on AI and the database you start with is the starting point. And then if someone comes in later and says they have the better database, that becomes a database migration, which is kind of a headache that we all know. Like -- so in that respect, like where are we on that winning back the base? What are the proof points that you see? Pablo, that's probably you, sorry. But where are we on that journey?

Sure. Raimo, it's good to see you again. Look, as I said, one of the key things for us that's super important is making sure that we are where the builders are. And I think as you know this, like we've always been a builder company. And so I went through what we're doing to show up from an awareness and from also an accuracy perspective. And we're seeing good early wins in terms of driving both of those. That's sort of the start of the funnel. We also need to be in all the right places for customers as they're building those applications. And frankly, like we are just getting into a lot of those marketplaces. And so -- and then it's creating a lot of that opportunity. So the coding agents can actually pick the best platform for the job where we are as it relates to JSON and the document model and also our integrated vector search and capabilities like Voyage. So that's a starting point. But the other thing I'll also say is when we've seen this with some of the at-scale customers, like we've had frontier lab that migrated to us in 4 weeks off of a PostgreS database for conversation history. And so the other thing also to think about it, as Dev said earlier, it's a new workload, and there's a lot of those that are coming up and spinning up. And so I think we are participating in that market, and it is in the enterprise and in production still pretty early. And the other part of this is we're also working very closely with a lot of the builder communities like the Emergent to make sure that we drive that awareness early on.

But Raimo, you've heard us talk and they were at our .local in London, ElevenLabs, where they started on Firestore and they just got to a point of scale as they saw their market opportunity increase and they said, we need to get off and got on MongoDB. And there's a whole host of other those that as a start-up, you're just trying to make your next paycheck, trying to find your product market fit. And then when they hit a scale point, they tip over really, and we're there to help them.

Let me just add one other thing, right? The way I like to think about this is the developers of today are different from the developers of 3 years ago when the tech stack was determined for them and then they started writing the code, right? Now Vibe coding, Claude Code, whatever makes it so they don't care about the tech stack until it's maybe too late. But if you think about the selection that happens in that tech stack decision, the model makes or the agent or whatever, pick your poison, it makes 2 decisions, one of them really cool and one of them sh****. One of them that's cool is it's always modeled on JSON, right? That makes our migration easier later on. The mistake that the models make is that it picks PostgreS on JSONB most of the time, which to Pablo's point, that's what we're trying to change. So when they scale and reach some sort of exit velocity just like Emergent did, right, easy migration, and we'll get them up here. So I'm happy with the enterprise. I'm happy at the mid-tier, we'll migrate them every single time, no problem. And now Pablo and the team were focused on the long tail of the funnel as well.

Matt Martino, Goldman Sachs. Dev, great to see you again. This question is for you. I wanted to talk about the modernization opportunity in AMP specifically because it started under your tenure. I guess what I'm curious about is because it seems like we went through a bit of a quiet period on AMP over the last kind of 9 to 12 months. So I'm curious what's changed from a technical and commercial standpoint where you can actually see some follow-through in the numbers around the modernization opportunity broadly?

Yes. So just to level set with everyone, what I started talking about maybe like 3 years ago was the fact that when we had lots of customers who felt trapped on legacy platforms and it was easy to move the data, it was very hard to rewrite the application code. And that was the big tax. And because these were a lot of old brittle systems, a lot of stored procedures, it was a very, very heavy complex job. With AI, we thought that, that problem would get solved. But what we underestimated, candidly, was that the maturity of the AI models took longer than we had expected. Obviously, we've seen rapid innovation of model capability and performance. And so Mike being the great CFO, he's good at managing expectations says, investors are a little tired about hearing AMP. So next time we talk about AMP, let's have something really hard to deliver. I would tell you that we're really excited about what we're seeing. We made an announcement of Cognition this morning, who's now our partner, who's helping us essentially work with us to help migrate and they've become very successful as a coding tool to help migrate customers and deal with that application logic problem. And obviously, we provide the tooling to migrate the data as well as put in all the guardrails to make that production system. So I think we didn't want to kind of overplay our hand today, but I think that's a really important growth vector. And as Mike talked about, it's one of the upside cases in our model.

Sanjit Singh, Morgan Stanley. I wanted to start with you, Dev, on -- I mean, we go back to the IPO and you were for years, consistently saying, we're about the developer, and we're focused on the developer. Obviously, the developer role is changing, right, from writing the code to orchestrating a harness. Do you see risk in that sort of changing roles of the developer and how MongoDB's value proposition ultimately changes? And what are you doing to mitigate that risk? And then a follow-up question for Ben on the -- what was the question here? On memory, is that like a net? Have you sort of productized that capability? Because I kind of thought that was part of the core value proposition of having a document model. So with the new agent enterprise offering, have you sort of productized a memory capability? Just want to get some clarity on that.

Yes. So Sanjit, to your first question, there's no question in my mind that over the next, say, x number of years, whether that's 3, 5 or 10, humans will no longer write code. I mean that -- you see that coming and it's become very clear. I think there may be human in the loop. I think there'll be kind of checks and verifications and all that, but most code will be written by agents. So yes, the original value prop of MongoDB, one of them was that it makes the developer's life so much easier because it's so consistent with the way they code and they think, especially we had all the drivers to all the popular languages. I would say what also was one of our value propositions was the ability to essentially scale up and scale down. A lot of the AI workloads we talked about is the bursty traffic. A lot of that traffic is ephemeral. We've always been a very scalable platform. So that plays to our strength. Ben mentioned JSON, the fact that we speak the language of AI. Everything -- you can even prompt in JSON. I don't know if people have actually done that, but you can prompt the LLM with JSON and it will respond because JSONs are -- I mean, LLMs are designed and built around JSON. So we think those attributes are just as important. And that's why we're building all this capability around like price performance, Infinite and Agent Engine to provide all the wrapping to enable agents. We recognize that we may have been a little slow like with other companies like Supabase who have done some work around making big much more agent friendly. Those are easy gaps for us to fill, and you'll see that us address that pretty quickly.

And just to then answer that. So there's memory of today, which I'll talk about, and then we'll talk about memory tomorrow or now today because we announced it today. Before today, when we talk about memory, you're 100% right. It was just another database workload for us. Agents are speaking JSON, great, stored in MongoDB. And that we saw that organically start happening at our customers as well as other context like chat conversations and other things. But the memory of today, what we announced today is a little different. It gets into more of the business logic.

Yes. So what we announced today, memory actually is a composable component. And as Ben said, we've got customers who have been using us for memory because they can and they've been building on top of it. But what we actually do -- and it's really -- it's both a memory and a compute problem. We actually bring in long-term memory. So all the stuff that comes in from previous conversations, stuff that gets inferred and brought back from the LLM. All that stuff comes in, and we actually break it down from a long-term perspective into 4 different types of memory. And so we do that under the covers for the customer. We break it down into procedural memory. So these are the steps that the agent will learn and memorize. Episodic memory, so the episodes and events that it's remembering. Taxonomic memory, which is the taxonomy that may be for that agent, for that team or for that organization, the terms that are very specific to that team like Magenta, the code word that we had for Agent Engine before we launched it and then semantic memory, which is sort of the knowledge of the organization. That all happens in the system, we aggregate it. And then because we have Vector Search Voyage under the covers, we're able to bring back the right context for the agent at the right time, which both drives accuracy, meaning fewer turns and lower cost as they're trying to drive some of the token usage. And so that's all available to customers. We provided as an SDK, and we're externalizing also through MCP.

Alex Zukin with Wolfe Research. A lot of great numbers, especially like the AI native ARR and the -- but I was a little -- I think we're all a little surprised about the magnitude of the higher Atlas or the confidence in that mid-20s over the next 3 years. So I mean, maybe just dive in a little bit between those 2 AI natives versus core, kind of what's driving that confidence? Do you expect to kind of reaccelerate the non-AI native part of the business, given to your point, now you have a CRO, you didn't have one in the first 6 months. Maybe just help us understand that, and I've got a quick technology question.

Sure. So as we look at Atlas, and we've said it multiple times in the last several quarters, we continue to feel very good about the Atlas business. Technically, going from 3% to 5% to 3% was a big thing, Alex. It allowed us to say, hey, what do we really see, call it, in the near term? And we've seen all the growth that has been driven by the move-up market, which has been significant, that as we look at the geo expansion, the new products, as well as the platform. And that is going to, over time, hopefully start to monetize, again, good usage, but it needs to monetize more as we look at that. And then, of course, you have the AI natives. So underneath the covers, I mean, look, at the AI natives are 3% to 5%, it means the core by definition as a percentage continues to call it, slow down, but the dollars continue to grow. And we look at that over the next 3 years, and we feel very good about growth in both of those. And we really haven't baked in a lot of upside for enterprise AI. It's the thing we talked to you folks the most about in the question earlier on when is that going to pick up? And at some point -- now keep in mind, AMP is great. They did a great job of going through that. We also want sales to be able to sell it. That's going to help both self-managed and Atlas but we do expect growth there. So the confidence is in the go-to-market engine, the growth we see in our larger customers, the new product and continued growth in AI natives.

If I could just add one other thing that I think it working, it also gives me confidence that I have the benefit of stepping back and now stepping back in is that when we launched 8.0, there was some concern both in the investor community, is that going to cannibalize some of your business, right? Because if you're providing a 30%, 40%, 50% faster machine, is going to need to use less gas. Well, it turned out that they wanted to drive that car more because they put more workloads on it because they saw so much benefit. And the fact that 80% of our fleet is already at 8.0 faster, 2x faster than any other prior release gives us confidence that, that same phenomenon will happen with 9.0 because 9.0 is that much more performant. And that's a pretty material data point.

Whether Atlas Infinite, where you feel like is there cannibalization risk concern? Is that completely net...

No. And let me address that. Ever since I've been at MongoDB, MongoDB has been viewed as a great technology. No one has ever questioned the caliber of the technology built. Some people have a question, are you -- some people use the term Ferrari or turbo sports car. It's great for certain workloads for the mainstream meat and potatoes, sometimes those internal workloads that don't need all the sizzle, maybe MongoDB is not the right profile for. By increasing the price performance, we make it just that much easier for people to put more and more workloads on MongoDB. So it helps our expansion to larger accounts because there's more workloads we can track. It helps our upsells and cross-sells in our existing customers. And obviously, it gets -- we can go after these new AI workloads as well.

[indiscernible] with Oppenheimer. Building on the enterprise AI opportunity, which you've talked about unlocking there, can you give us some perspective on what you think the system integrators can do for you when you start expanding there?

Yes. For me, the systems integrators are about credentializing us, giving us access to buyers that we would not have otherwise faced off against and helping us drive true transformation, right, at the business level. That's what the SIs are going to be able to help us do. And what's important for us, and you heard it from Vikas, right, and this is where it's actually coming true, so it is an example, is a lot of our enterprise customers are reliant on the system integrators to help them make the decision around what technology and what innovations they want to use for their AI transformations because there's so much innovation out there, it's complicated to make those decisions. And so getting with the SIs and getting embedded into their frameworks, they're giving us a natural entry point into those buyers, into those enterprises. And we're credentialized from the start because they're walking in and saying, as part of our stack, we're embedding MongoDB. So for Infosys, we're part of Topaz's framework now. So when anyone walks into their innovation lab around the world, on the wall, it's going to say MongoDB. And when they show Topaz, it's going to say, we are the data platform for it. So that gives us access that we might not already have. And that's going to be true expansion for us in terms of new customers, new stakeholders and new revenue.

Yes. And I also want to add just like one of the things that surprised me, it kind of hasn't surprised me, but in some ways surprised me is that the gap between where the AI capabilities are and where the enterprises are is wide. there's a massive what people call the diffusion gap. And it kind of -- if you go back in history and look at like when PCs were deployed and networking was deployed, it took time for people to reengineer their work around this new technology and the productivity gains didn't really happen until 3, 4, 5, 6 years post the deployment of the technology. I definitely see having been at Sequoia and I was working with them over the summer, like this -- clearly, the AI natives are all AI pilled and they're being very, very aggressive. But when you talk to the enterprises, they're still moving very slow. I mean the examples I gave you are some very sophisticated customers, they're just pleased to have an AI chatbot, right? And so it's going to take some time for them to really deploy and leverage the sophistication. In the last month, we've seen more AI models deployed with performance gains that are unbelievable. And each one of them would have been like a noteworthy front page of newspaper announcement 12 months ago. And now you just see them coming out almost every week. And these enterprises just can't keep up. So the SIs play an important role in kind of bridging that gap between where the capabilities are and where the customers are. And I think that's where they're going to add a lot of value.

It's Steven Koenig from Macquarie. I want to build on the good question that you answered earlier, Dev, about the nature of the developer changing in the future. And I want to bring together a couple of points that I've heard here. One is that the agents are currently like going with PostgreS with their JSON extensions instead of being aware of Mongo. And I want to also bring in your point about your working harder to move up the decision-making ladder in the enterprise. And then lastly, I just want to bring in the fact that like, Mike, when you were citing like 3 of the top use cases for agentic, the coding use case, which is huge in the enterprise and AI natives, wasn't one of those 3 use cases. And so it smells like there's an opportunity here to enable the coding agents in creating applications and enabling them to decide to use Mongo and putting that in at an enterprise level. I don't know if I'm stretching this too far, but like how will Mongo -- is there an opportunity here? And if there is, how would you look to enable this?

Pablo, do you want to take...

Yes, I'll start. Look, I think as you know, in the enterprise, you have your enterprise approved solutions, and we fare very well in that space. One of the things that we hadn't been doing well is making sure that we show up in the coding agents, making it super easy for developers or for the -- in the coding agents, making it super easy for developers or for the executives to choose a framework. As you know, in enterprise, a lot of those decisions are getting made by enterprise architects in terms of like for their production at scale systems. And we've made that a lot easier for them. We made it a lot easier for those systems. And one thing I'll highlight in our partnership with Cognition, where they have -- they're getting a lot of reach in some of these enterprises, we're actually seeing that happening. And those choices both the coding agent and the platform is being made together. When you think about it, in an enterprise, like they're not going to let developers build production scale applications using any single solution. Those typically go through the architecture team. And that, I think, one, we show up well there. But really, a lot of the stuff that we've been doing for both builder awareness, accuracy and being in those platforms is really helping us make it super easy for those teams to build on us.

Yanni Samoilis with Baird. So just going back to the enterprise AI transformations for a moment. Those can be super lucrative, but also they require a lot of handholding. The SIs can help with that, of course. But wondering if you think you need to send more of your technical folks in as well, embed them in those customers' operations to be able to help with some of that handholding or if the SI partnerships are sufficient enough?

It's going to be both is the answer. I mean we are building FDE capability right now, starting in our product teams, it will migrate over to professional services. We'll have that capability, and we'll be able to deploy that alongside our partners and our customers to help them accelerate their revenue. So we absolutely believe that's necessary. We know our customers, they have to set up their schemas, their indexes, everything correctly in order to make sure they get maximum value of the platform and to make sure that then they want to consume more. So that's going to be critical. It's a motion we're driving. And frankly, it's essential for delivering the kind of customer experience we want and getting the level of consumption we need.

And the thing I'll add to that, and this is true for both AMP and for Agent Engine where we do have FDE capabilities. It's not just about helping the customers. It also creates a super tight feedback loop for us as we've been in design partnerships and in our early previews with a lot of these customers, those FDEs are embedded. They're there, they're either physically there or in with those teams, and we're getting that feedback back super quickly, which is helping us reduce the cycle time for new innovation.

This is [indiscernible] for Miller Jump from Truist. So I just have one quick one on EA. And EA has been doing really well in the past few quarters, and it's benefiting a lot from Vector Search. Given the double-digit growth opportunity in the next 3 years, could you just talk about the opportunity? And where do you expect EA to continue benefiting from?

Yes. So as I articulated about the Run Anywhere strategy and vision, that is 100% something we're committed to. We're going to continue doing that, which basically means as we do more and more inside of cloud and other things inside of Atlas, where it makes sense, we'll continue making more parity decisions to bring those into the self-managed, I would say, ecosystem. But just from a market perspective, there's a lot of things going on. We talked about AI adoption in enterprises. Some of it is people, some of it is laws that haven't even been passed. They don't know what they're doing in France. They don't know what they're doing in England. How does GDPR going to apply to this? Does FDA have to approve this model for this particular use case? Does it -- do we have to give an agent a Series 7 so they can look at financial data and give advice to your customers, right? We don't know what this actually looks like. That's -- when they figure out all that, that's when we're going to see enterprise AI like go really crazy. Until then, we're going to see what we're seeing as well as where EA plays into a part is, one, regulated industries where they can run it on inside of their fiefdom and then make it for internal -- more internal use cases. Two, public sector in like air-gapped environments because that's really important. They're not going to slow down. And then three, there is plenty of geo locations in the world that doesn't have a cloud provider that either needs to be served by a Neocloud or an Equinix or pick your poison or just doesn't even have anything, so it has to go there. But the banks or the insurance companies still want to serve that area. So EA is going to play a part there. So EA really has a massive play in this thing, and it's not an or, it's 100% an and.

One thing I'll add to that, EA is a deployment option for us. As I talked about -- and Search and Vector Search was introduced recently in the last couple of months. So when I talked about the Vector Search growth over the last couple of years, it really was around our cloud. And the thing there, as Mike touched on, like those multi-feature use cases really do resonate with customers. And so one thing I think about is like we've seen the match and we've seen the market fit as we've deployed this in Atlas. And so as we go into EA, we're earlier in the journey, but we're very optimistic.

We're up on time. Thank you, everyone, for the great questions.

Thank you. Thanks, everybody.